Long-term label-free assessments of individual bacteria using three-dimensional quantitative phase imaging and hydrogel-based immobilization

Three-dimensional (3D) quantitative phase imaging (QPI) enables long-term label-free tomographic imaging and quantitative analysis of live individual bacteria. However, the Brownian motion or motility of bacteria in a liquid medium produces motion artifacts during 3D measurements and hinders precise cell imaging and analysis. Meanwhile, existing cell immobilization methods produce noisy backgrounds and even alter cellular physiology. Here, we introduce a protocol that utilizes hydrogels for high-quality 3D QPI of live bacteria maintaining bacterial physiology. We demonstrate long-term high-resolution quantitative imaging and analysis of individual bacteria, including measuring the biophysical parameters of bacteria and responses to antibiotic treatments.


Scientific Reports
| (2023) 13:46 | https://doi.org/10.1038/s41598-022-27158-y www.nature.com/scientificreports/ geometry during measurements. In addition, during long-term measurements, bacteria often travel from their original locations, occasionally out of the field of view (FOV). Several studies have suggested immobilization techniques to circumvent motility-related issues when measuring individual bacteria. One technique utilizes poly-l-lysine, which is frequently used to facilitate cell adhesion to plasticware or glass surfaces 29 . However, the physiological alteration of bacteria due to poly-l-lysine restricts the utility of this technique 30 . Another bacterial immobilization technique is an agar pad [31][32][33] . However, when applied to 3D QPI, an agar pad produces noisy images [ Fig. 1] because of the inhomogeneous distribution of RI in an agar pad.
To achieve 3D QPI measurements of bacteria with neither motion-induced problems nor background noise, we propose an experimental method that utilizes a hydrogel to improve the imaging quality. Hydrogels are cross-linked hydrophilic polymers that form optically clear extracellular matrices and are suitable materials for addressing the aforementioned issues 34,35 . Here, we experimentally demonstrate a method for imaging bacteria using hydrogel substrates and optical diffraction tomography (ODT), a 3D QPI technique 36 . Our method achieves higher image quality and stable sample immobilization compared to using a liquid medium or an agar pad environment. Moreover, our method facilitates long-term analyses of cellular features and investigation of the response to antibiotics. Based on the advantages emphasized in our study, our method provides a high-quality auxiliary technique for the study of individual bacteria.

Results
Measurements of high-quality ODT using a hydrogel environment. To validate the imaging capability of the hydrogel-based environment in 3D, we compared the 3D QPI images of individual bacteria obtained in a liquid medium, agar pad, and hydrogel [ Fig. 2]. We first validated that a liquid medium and hydrogel have identical background RI values, rendering the common reconstruction and analysis available for hydrogel-based measurements. We experimentally verified the homogeneous background RI distribution of liquid and hydrogel medium by comparing the phase delay maps of 2 μm SiO 2 beads, lying in both media [ Supplementary Fig. S1].
The motion artifact in the hydrogel-based environment was dramatically reduced compared with that in the liquid environment. In liquid media, large numbers of bacteria exhibited motion during measurement producing motion artifacts. The property of this motion in liquid media was further specified by analyzing the   To further support hydrogel's bacteria immobilization capacity, we calculated the viscosity of a liquid medium and a hydrogel medium, which were 0.001 ± 0.000 Pa•s and 0.286 ± 0.120 Pa•s, respectively. Thus, our hydrogel-based protocol facilitates more accurate profiling of individual bacteria without the need for tedious data curation. For the remaining analyses in liquid media, we utilized curated tomograms without motion artifacts to address further issues, including background noise and motility. We examined the background noise level of the 3D QPI image and the degree of bacteria immobilization in the liquid, agar pad, and hydrogel environment [ Fig. 3]. 3D RI tomograms were measured for 1.5 h in each medium environment and visualized using MIP with the corresponding bacterium centered in the image. K. pneumoniae in the liquid medium traveled and rotated [ Fig. 3a], whereas bacteria in the agar pad and the hydrogel were fixed, enabling monitoring of growth in different directions [ Fig. 3b,c]. The hydrogel environment allows for reliable visualization and measurements of bacterial growth and division. The increment of background noise over time is due to mechanical instability and medium state alteration.
Moreover, we analyzed the RI distribution of background by computing the mean squared error (MSE) [Fig. 3d]. The median values of MSE for a liquid medium, agar pad, and hydrogel were 1.69 × 10 −7 , 1.31 × 10 −6 , and 1.99 × 10 −7 , respectively. The Wilcoxon rank-sum test p-values were 0.0013, 0.0023, and 0.7175 for liquid and agar pad, for agar pad and hydrogel, and for liquid and hydrogel, respectively. Here, the MSE values of the background for a hydrogel system are comparable with that of the liquid medium with a high p-value. By comparison, the agar pad system recorded the highest MSE values of background RIs. The results show that the www.nature.com/scientificreports/ hydrogel is an appropriate system for bacteria immobilization while maintaining noise levels within the range of the liquid medium. This reduced noise level of the hydrogel can also lead to more precise monitoring of quantitative features including the dry mass density, compared to the agar pad. That is, due to the linear relation between the RI increment and dry mass density, variation in the quantitative features can be suppressed with precise measurement of RI.
Biological compatibility of a hydrogel as a medium for bacteria culture. We then compared bacterial division in the hydrogel environment and in the liquid medium to assess the effect of hydrogel on the physiology of bacteria [Fig. 4]. No drastic physiological changes were observed, as the doubling time of bacteria in the two environments did not vary significantly. In particular, we investigated cell morphology and the distribution of the doubling time of K. pneumoniae in the liquid medium and in various concentrations of the hydrogel [ Fig. 4]. We acquired RI tomograms at 1 min intervals and measured bacterial growth and division in the MIP images. Timepoints at division and at 3 min before and after division were chosen for the morphological analysis [ Fig. 4a]. MIP images show that bacteria retain similar morphologies, grow to a fixed size, and reproduce through binary fission without any abnormal alterations in all conditions. In a successive step, the doubling time of K. pneumoniae was obtained to quantitatively determine the effects of hydrogel on biological metabolism. The division events of 57, 50, 61, and 53 bacteria were investigated for each medium environment: liquid medium, hydrogel 70%, hydrogel 80%, and hydrogel 90% medium. To improve accuracy, we performed three separate examinations for each condition.
The distributions of the doubling time of K. pneumoniae in the various media were comparable. The average and standard deviation of doubling time of K. pneumoniae in the liquid medium, hydrogel 70%, hydrogel 80%, and hydrogel 90% were 30.75 ± 4.58 min, 34.42 ± 6.95 min, 30.20 ± 3.98 min, and 29.53 ± 4.13 min, respectively [ Fig. 4b]. The Wilcoxon rank-sum test p-value for the liquid and the pooled hydrogel media was 0.49, indicating statistical similarity. Moreover, we calculated the dry mass density distributions of the bacteria at 0 min in a liquid, an agar pad, hydrogel 70%, hydrogel 80%, and hydrogel 90% medium to further validate the constant physiological state of bacteria in hydrogels [ Fig. 4c]. Total 99, 28, 91, 28, and 68 bacteria were calculated for a liquid, an agar pad, hydrogel 70%, hydrogel 80%, and hydrogel 90% medium, where the average and standard deviation values of dry mass density distributions were 11.83 ± 0.71 g/dL, 9.14 ± 0.54 g/dL, 11.75 ± 0.73 g/ dL, 12.04 ± 0.56 g/dL, 11.53 ± 1.01 g/dL, respectively. The dry mass density distribution of bacteria in a liquid medium was comparable to the pooled dry mass distributions of bacteria in hydrogel 70%, hydrogel 80%, and hydrogel 90% medium with the Wilcoxon rank-sum test p-value of 0.3 [ Fig. 4c]. In contrast, the dry mass density www.nature.com/scientificreports/ distribution of bacteria in an agar pad represents a decrement compared to the others, with the Wilcoxon ranksum test p-value of 8.30 × 10 −36 between a liquid medium environment. We also note that the precision of the dry mass measurement is directly related to the experimental precision of RI and the imaging resolution, which are decisive for the dry mass density and the volume, respectively.
Cell tracking efficiency of hydrogel when yielding cellular features. Our hydrogel-based protocol facilitated the 1.5 h monitoring of a single bacterium, allowing the investigation of morphological and biochemical features over time. The following cellular features were retrieved and analyzed using 3D RI tomograms: cellular dry mass, volume, and surface area. The temporal changes in these features were plotted to quantitatively assess bacterial growth in both the liquid and hydrogel media [ Fig. 5b,d].
The growth of K. pneumoniae could be continuously tracked in the 80% hydrogel medium; this was difficult to accomplish in the liquid medium because of bacterial motility. The values of the cellular features in the liquid medium show sudden drops at certain points [ Fig. 5b]. A review of acquired images showed two cases that hindered continuous growth measurements in the liquid medium. In the first case, the motility and rotation of the daughter cells rendered it difficult to trace their origins. In the second case, one of the daughter cells moved out of the field of view [Fig. 5a, D 21 at 71 min]. In contrast, the hydrogel medium becomes a practical environment for continuously tracking and analyzing cellular features. Bacteria are trackable and well immobilized for all Further application for imaging-based antimicrobial susceptibility testing. Finally, we demonstrated the potential application of hydrogel as an imaging medium for imaging-based antimicrobial susceptibility testing (AST) 38,39 [ Fig. 6]. We treated K. pneumoniae and E. coli with 200 μg/mL of ampicillin in the 80% hydrogel medium and measured 3D RI tomograms for 90 min to assess whether the hydrogel-based environment is suitable for evaluating bactericidal responses 25 . As a result of ampicillin treatment, alterations of K. pneumoniae in the biochemical and morphological properties were identified. First, the treatment of ampicillin led to an immediate drop in the dry mass density. The dry mass density in an 80% hydrogel medium treated with ampicillin at 0 min was significantly decreased to 10.33 g/ dL [ Fig. 6c] while that in an 80% hydrogel medium without ampicillin had an average value of 12.04 ± 0.56 g/dL [ Fig. 4c]. Also, a prolonged elongation of K. pneumoniae was observed. Even after the duration of time that is sufficient for cell division, the cell length kept increasing without division as shown in the MIP images [ Fig. 6a,  63 min]. The formation of a high RI bulge in the middle of the cell was also visible [Fig. 6a, 89 min], which has been reported in previous studies as the result of the inhibited transpeptidase 40,41 . Figure 6b shows the MIP of a single E. coli treated with ampicillin. Over time, a bulge formation can be detected along with no increment of dry mass values [Fig. 6b, 60 min and Fig. 6c] 40 . Based on these observations, we conclude that the hydrogel can be used successfully to image the physical responses of bacteria to antibiotics.

Discussion and summary
We propose and demonstrate a protocol for 3D QPI of bacteria that does not suffer from motion-induced artifacts or background noise. Our protocol utilizes hydrogel as part of the loading medium and benefits from the formation of optically clear extracellular matrices 34,35 . The hydrogel facilitates high-quality 3D QPI measurements of bacteria by immobilizing bacteria without generating noise. Moreover, the physiological characteristics of samples in a hydrogel system are invariable and readily accessible for analysis as indicated by our statistical analysis of the doubling time and dry mass density. The biocompatible and steady environment created by our protocol enabled long-term quantitative measurements. Further experiments also suggest the feasibility of our protocol in practical applications, such as measuring the efficacy of antibiotics in a hydrogel environment based on 3D QPI. We believe that our protocol would facilitate the applications in which 3D morphology measurement is crucial as illustrated in the following. Identification of pathogenic bacteria is a potential application of the proposed method. Time-consuming microbial culture is an obstacle to early appropriate antibiotic treatment due to the need for ensemble behavioral data of myriad bacteria upon antibiotics 42 . Single-cell techniques, including QPI, aid in overcoming this limitation by measuring responses from individual bacteria 13,43 . And our method can support bacterial identification with QPI by providing a more stable environment for measurements. Our method can be further applied in a high-quality imaging pipeline for the QPI-based identification of bacteria samples. An earlier study has demonstrated the rapid identification of bacteria using 3D QPI and deep learning 13 , and we believe that our protocol can boost data acquirement and enhance the performance of such techniques by reducing motion artifacts and enabling long-term imaging. In addition, the classification of bacteria based on biomarkers will also be viable using our method 43 , as it provides a more stable acquisition of biomarkers, including dry mass and volume. Therefore, our protocol will propel 3D QPI for rapid bacterial identification.
The study of bacterial responses in different environments is another field to which our protocol can contribute. One practical application of this method is AST. AST is used to assess the antibiotic resistance of specific bacteria. This information is crucial for medical doctors to determine the types of antibiotics used for infection treatment 44 . Traditional AST utilizes populational information of bacteria when measuring their susceptibility to antibiotics 45 . Our protocol can be implemented for not only population-based AST 24 , but also for assessing the drug susceptibility of individual bacterium, which has been indicated as a rapid alternative to the conventional time-consuming approaches 28 . A commercialized label-free individual bacteria AST method evaluates samples in an agarose-based environment using bright-field microscopy. This method has the advantage of label-free imaging but still entails a day-long delay owing to the requirement of incubation 46,47 . This long turnaround time is not present in 3D QPI-based AST due to the high sensitivity and accessibility to quantitative biophysical properties. In visualizing the morphology, 3D QPI provides further contrast and sensitivity than bright-field microscopy, as cells typically induce more diffraction than absorption in the visible wavelength. Furthermore, 3D QPI provides not only the morphological information as bright-field microscopy does, but also additional quantitative information, including RI distribution, dry mass, and dry mass density, which are suitable for single-cell analysis such as cell death 48 . Using our approach, 3D QPI monitoring of individual bacteria can reveal antibiotic susceptibility within approximately an hour. The application of our method in 3D QPI is especially efficient when it comes to AST for unculturable or hardly culturable bacteria. For instance, Treponema pallidum subsp. pallidum and Synergistetes strains, which are the major causative agents of syphilis and periodontal diseases 49,50 , are arduous to culture in the laboratories as they require long and unique culture conditions, special reagents, and co-culturing with specific cell lines. Those bacteria can be tested in time-and resource-efficient ways by accurately analyzing individual bacteria with 3D QPI and our protocol. Therefore, we envision that our protocol can accelerate labelfree AST by eliminating the need for time-consuming cultivation procedures. Furthermore, a more efficient AST based on 3D QPI and our method can be realized by introducing microfluidic techniques. By forming drug gradients in a microfluidic chip, an on-chip AST based on 3D QPI monitoring can be facilitated.
Our method can also benefit studies of microbial biopolymer synthesis by quantitatively monitoring the production levels of each cell. Bacteria can be used as cell factories for biopolymers, which are then used in medical and industrial applications 4 . By monitoring individual biopolymer production levels non-invasively, more efficient conditions for biopolymer production can be applied at medical and industrial levels 28 .
Moreover, our protocol can aid in cellular mechanism studies of individual bacteria. Single-cell analysis with 3D QPI based on our protocol would enable the phenotyping of individual bacterium depending on their cellular mechanism states. Seemingly uniform bacterial populations for research may differ in cell cycle states, and genotypic and phenotypic subpopulations to their neighbors 51 . Our method can facilitate investigating heterogeneous cellular physiology at single-bacterium resolution by providing stable long-term analysis for both qualitative morphology and quantitative RI information of individual live cells 16,52 .
We plan to investigate the capability of our method extensively. Although we measured each specimen within 1.5 h, with more precise maintenance of temperature and humidity, a much longer investigation would be possible. In addition, a hydrogel-based environment can be implemented in a shorter time using different polymer compositions, as already suggested by several studies 53,54 . To extensively utilize the proposed method for AST, the influence of hydrogel in the diffusion of antibiotics should be investigated 55 . Ampicillin, which was utilized in our demonstration, is unlikely to be impeded during diffusion due to its small molecular weight. However, recent studies have also suggested macromolecular antibiotics, of which the diffusion dynamics may vary due to mesh environments such as hydrogel 56  Agar pad preparation. Agar pads are agarose gels that solidify in thin cuboid forms. Here, we adopted the gold standard protocol for bacteria imaging on an agar pad, in which bacteria are settled between an agar pad on the bottom and a glass cover glass on top [57][58][59] . We prepared a 20 mm × 20 mm parafilm and excised a square hole of 12 mm × 12 mm. The parafilm was placed on a TomoDish and fixed by partial melting on a hot plate at 70 °C. Fully melted 1.5% agarose gel (214010, BD, Franklin Lakes, New Jersey, USA) was poured onto the TomoDish and coverslipped. After the agarose gel was fully solidified, the coverslip was removed without generating any tears or wrinkles on the agar pad. Next, 5 μL of a sample was dropped on the agar pad, which was then gently covered with a new coverslip. Finally, the chamber was sealed by adding 10 μL of mineral oil to the grooves and incubated for 10 min on a 37 °C hot plate. Approximately 30 min were required for the sample preparation on an agar pad including the incubation.

RI diffraction tomography.
To acquire a 3D RI tomogram of the bacteria, we used an ODT instrument (HT-2H, Tomocube Inc., Daejeon, Korea) with a live cell imaging chamber (Tomochamber, Tomocube Inc., Daejeon, South Korea) which provides a moist environment and maintains the temperature and CO 2 concentration of the sample to 37 °C and 5%, respectively. The variability in the RI of water can be expressed with an empirical equation along with temperature change and empirical coefficients but is negligible in our measurement condition 60,61 . This optical system is based on a Mach-Zehnder interferometric microscope with a digital micromirror device (DMD) to control the angle of the sample beam [ Fig. 1b] 62 . A coherent laser beam (λ = 532 nm in vacuum) was split into sample and reference beams using a 2 × 2 single-mode fiber optic coupler. The DMD controls the angle of the sample beam impinging on a sample. The imaging exposure time is around 0.15 s considering 49 DMD patterns and 3.019 ms of camera shutter time length. The beam diffracted by a sample is imaged on the camera plane, where it interferes with the reference beam. Using the phase-retrieval algorithm, the phase and amplitude images were retrieved from each interferogram. The 3D RI distribution was reconstructed from various 2D holograms of the sample from different illumination angles and by inversely solving the Helmholtz equation with the Rytov approximation. The theoretically calculated lateral and axial spatial resolutions of the optical imaging system were 110 nm and 360 nm, respectively 63 . Because of the limited numerical aperture of condensers and objective lenses, side-scattering signals were not collected, leading to image quality degradation of the reconstructed 3D RI tomograms. To resolve this missing cone problem, an iterative regularization algorithm based on a non-negative constraint was used 64 . The detailed implementation and reconstruction code were as published previously 65,66 .

Data availability
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.